Filters








3,511 Hits in 8.5 sec

Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

Stephanie P. Goldstein, Brittney C. Evans, Daniel Flack, Adrienne Juarascio, Stephanie Manasse, Fengqing Zhang, Evan M. Forman
2017 International Journal of Behavioral Medicine  
The primary aim of this paper is to illustrate the use of this framework as it pertains to developing a JITAI that targets lapse behavior among individuals following a weight control diet.  ...  Method-Just-in-time adaptive intervention (JITAI) is an intervention design framework that could be delivered via mobile app to facilitate in-the-moment monitoring of triggers for lapsing, and deliver  ...  Since this data was still comprised of substantially more non-lapse than lapse cases (unbalanced data), non-lapse cases were randomly sampled in a 1:1 ratio to lapse cases via the ROSE package in the R  ... 
doi:10.1007/s12529-016-9627-y pmid:28083725 pmcid:PMC5870794 fatcat:6d4qxza2zfcjxbtqvjno6k6xza

2021 Index IEEE Open Access Journal of Power and Energy Vol. 8

2021 IEEE Open Access Journal of Power and Energy  
Note that the item title is found only under the primary entry in the Author Index.  ...  The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Yin, H., +, OAJPE 2021 389-398 Quasi-Static Time-Series Power Flow Solution for Islanded and Unbalanced Three-Phase Microgrids.  ... 
doi:10.1109/oajpe.2021.3139293 fatcat:q6vpjeyynrb6dbu4dvewla5mgi

A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars

Hawzhin Hozhabr Hozhabr Pour, Frédéric Li, Lukas Wegmeth, Christian Trense, Rafał Doniec, Marcin Grzegorzek, Roland Wismüller
2022 Sensors  
Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors.  ...  Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars .  ...  The findings and conclusions of this paper are those of the authors and do not necessarily represent the views of the VTTI, SHRP2, the Transportation Research Board, or the National Academy of Sciences  ... 
doi:10.3390/s22103634 pmid:35632039 pmcid:PMC9146681 fatcat:7mhpghfclrdfjiyzwxdqvm3jwe

EVScout2.0: Electric Vehicle Profiling Through Charging Profile [article]

Alessandro Brighente, Mauro Conti, Denis Donadel, Federico Turrin
2021 arXiv   pre-print
In this paper, we propose EVScout2.0, an extended and improved version of our previously proposed framework to profile EVs based on their charging behavior.  ...  To the best of the authors' knowledge, these results set a new benchmark for upcoming privacy research for large datasets of EVs.  ...  However, if an ascending segment is found after a descending samples series, the counter is reset.  ... 
arXiv:2106.16016v1 fatcat:6kagb5yenvgdpdr3e6jlynxwhy

Intelligent Fault Detection and Classification Based on Hybrid Deep Learning Methods for Hardware-in-the-Loop Test of Automotive Software Systems

Mohammad Abboush, Daniel Bamal, Christoph Knieke, Andreas Rausch
2022 Sensors  
To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model.  ...  Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22114066 pmid:35684686 pmcid:PMC9185421 fatcat:lz7zs37nxjfuxinigmdssql25a

Using Neural Networks by Modelling Semi-Active Shock Absorber [article]

Moritz Zink, Martin Schiele, Valentin Ivanov
2022 arXiv   pre-print
The approach is based on the adaptation of time series augmentation techniques to the stationary data that increases the variance of the latter.  ...  In this regard, the paper presents an approach, which demonstrates how the regression tasks can be efficiently handled by the modeling of a semi-active shock absorber within the DT framework.  ...  While there are some established approaches for unbalanced classification problems (SMOTE, ClusterCentroids etc.), the selection of the procedures for solving regression problems is a non trivial task.  ... 
arXiv:2207.09141v1 fatcat:tujmf7txdzbihkqfnp32y6sibe

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

Zafar Iqbal, Majid I. Khan, Shahid Hussain, Asad Habib, Átila Bueno
2021 Complexity  
Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers  ...  The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when  ...  Acknowledgments e authors would like to thank the United States (US) Government and Chicago Transit Authority (CTA) for providing the road incidental data.  ... 
doi:10.1155/2021/5543698 fatcat:hvjnzekvobetrow4vkq36iejky

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning [article]

Lichao Sun, Yuqi Wang, Bokai Cao, Philip S. Yu, Witawas Srisa-an and Alex D Leow
2017 arXiv   pre-print
., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification.  ...  Currently, the use of cookies is the most common form to identify users.  ...  As GRU extracts a latent feature representation out of each time series, the notions of sequence length and sampling time points are removed from the latent space.  ... 
arXiv:1711.02703v2 fatcat:aqqm3n3hgfgblls3xcbtlqo4cu

TRI-CYCLES ANALYSIS ON BANK PERFORMANCE: PANEL VAR APPROACH

Denny Irawan, Febrio Kacaribu
2017 Buletin Ekonomi Moneter dan Perbankan  
We examine the responses of individual bank credit cycle and risk cycle toward a shock in business cycle macro risk and its consequence to the bank performance.  ...  The result shows dynamic relationship between business cycle macro risk and financial risk cycles. The study also observes prominent role of risk cycles in driving bank performance.  ...  The result show dynamics of financial cycle -in the form of credit cycle and risk cycle -preceded the business cycle macro risk.  ... 
doi:10.21098/bemp.v19i4.694 fatcat:tboko3mxxrehzl66doo3fqooqm

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review [article]

Abolfazl Razi, Xiwen Chen, Huayu Li, Hao Wang, Brendan Russo, Yan Chen, Hongbin Yu
2022 arXiv   pre-print
Driving Systems (ADS)-equipped vehicles, and highlight the missing gaps.  ...  a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step.  ...  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
arXiv:2203.10939v2 fatcat:oml733wvjfh3blne4h7kg5y3du

Longitudinal Modeling of Insurance Claim Counts Using Jitters

Peng Shi, Emiliano A. Valdez
2011 Social Science Research Network  
The predictive distribution together with the corresponding credibility of claim frequency can be derived from the model for ratemaking and risk classification purposes.  ...  For empirical illustration, we analyze an unbalanced longitudinal dataset of claim counts observed from a portfolio of automobile insurance policies of a general insurer in Singapore.  ...  Shi (2011) modified the t-plot method for the case of unbalanced data and adapted the framework for both in-sample and out-of-sample validation purposes.  ... 
doi:10.2139/ssrn.1926237 fatcat:ar5qllaisbeo5pcet63ewlnnxi

Longitudinal modeling of insurance claim counts using jitters

Peng Shi, Emiliano A. Valdez
2012 Scandinavian Actuarial Journal  
The predictive distribution together with the corresponding credibility of claim frequency can be derived from the model for ratemaking and risk classification purposes.  ...  For empirical illustration, we analyze an unbalanced longitudinal dataset of claim counts observed from a portfolio of automobile insurance policies of a general insurer in Singapore.  ...  Shi (2011) modified the t-plot method for the case of unbalanced data and adapted the framework for both in-sample and out-of-sample validation purposes.  ... 
doi:10.1080/03461238.2012.670611 fatcat:h6rsd7pup5gt5hnx6idqypkn5i

Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Drew Creal, Bernd Schwaab, Siem Jan Koopman, Andre Lucas
2011 Social Science Research Network  
In 2013 all ECB publications feature a motif taken from the €5 banknote.  ...  Acknowledgements The views expressed in this paper are those of the authors and do not necessarily reflect the views of the European Central Bank or the European System of Central Banks.  ...  for the modeling of a large unbalanced panel of time series.  ... 
doi:10.2139/ssrn.1765764 fatcat:4fiee2qohnf3pomaqgmojp27ly

A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence [article]

Manoj Basnet, Mohd. Hasan Ali
2021 arXiv   pre-print
This book chapter entails the cyber-physical vulnerabilities and risks that originated in IT, OT, and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence.  ...  Our results indicate the superiority of the proposed model although DNN and 1d-CNN also achieved more than 99% of accuracy, precision, recall, f1-score, and AUC on the CAV-KDD dataset.  ...  Being able to capture the local patterns, 1D CNN is the popular algorithm for time series classification/regression successfully tested in the fields of natural language processing, audio industry, and  ... 
arXiv:2109.10763v1 fatcat:tigj5x6pnrbmrbf3my46ynr4we

Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach

David Castiñeira, Katherine R Schlosser, Alon Geva, Amir R Rahmani, Gaston Fiore, Brian K Walsh, Craig D Smallwood, John H Arnold, Mauricio Santillana
2020 Respiratory care  
sample.  ...  We introduced a methodology designed to automatically extract information from continuous-in-time vital sign data collected from bedside monitors to predict if a patient will experience a prolonged stay  ...  The authors have disclosed no conflicts of interest. The study was performed at Boston Children's Hospital, Boston, Massachusetts. Dr Geva was funded by NICHD T32 HD040128 and NICHD K12 HD047349.  ... 
doi:10.4187/respcare.07561 pmid:32879034 pmcid:PMC7906608 fatcat:rzk25oz3yjen5ejjq44jjpb4tq
« Previous Showing results 1 — 15 out of 3,511 results